About this book

Introduction

In this monograph, new structures of neural networks in multidimensional domains are introduced. These architectures are a generalization of the Multi-layer Perceptron (MLP) in Complex, Vectorial and Hypercomplex algebra. The approximation capabilities of these networks and their learning algorithms are discussed in a multidimensional context. The work includes the theoretical basis to address the properties of such structures and the advantages introduced in system modelling, function approximation and control. Some applications, referring to attractive themes in system engineering and a MATLAB software tool, are also reported. The appropriate background for this text is a knowledge of neural networks fundamentals. The manuscript is intended as a research report, but a great effort has been performed to make the subject comprehensible to graduate students in computer engineering, control engineering, computer sciences and related disciplines.

Keywords

MATLAB algorithms control control engineering learning algorithm modeling neural networks robotics

Bibliographic information

  • DOI https://doi.org/10.1007/BFb0047683
  • Copyright Information Springer-Verlag London Limited 1998
  • Publisher Name Springer, London
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-85233-006-4
  • Online ISBN 978-1-84628-527-1
  • Series Print ISSN 0170-8643
  • Series Online ISSN 1610-7411
  • About this book